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首页> 外文会议>International Conference on Frontiers of Biological Sciences and Engineering >Multi-classification of Cardiac Diseases Utilizing Wavelet Thresholding and Support Vector Machine
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Multi-classification of Cardiac Diseases Utilizing Wavelet Thresholding and Support Vector Machine

机译：利用小波阈值和支持向量机的心脏病多分类

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摘要

Automatic classification of electrocardiogram (ECG) is importance in cardiac disease diagnosis. Support vector machine (SVM) has drawn more and more attention on pattern recognition, including ECG feature extraction and cardiac disease detection. The most prominent advantage of SVM can be represent as its excellent performance on simplification of inner product operation from high dimensional space to low dimensional space, avoiding calculations in high dimension space. In this study, a multi-classification method is proposed utilizing wavelet multi-resolution analysis (WMRA) and SVM. WMRA is applied to eliminate interference with frequency beyond the frequency interval of ECG signals (0.05~100Hz). Meanwhile, WMRA provides detail coefficients and approximation coefficients of different decomposition levels, which are the input features fed into SVM for classification. After that, SVM is employed to recognize 6 types of cardiac beats from MIT-BIH arrhythmia database. Besides, different parameters C and y are discussed and tested. Experimental results indicate that the classification performance gets better as C increases and y decreases. When C and y are set to be 1000 and 0.1 respectively, an overall classification accuracy, sensitivity and positive predictivity of 95.23%, 97.42% and 97.71% respectively are achieved.
机译：自动分类心电图（ECG）是心脏病诊断的重要性。支持向量机（SVM）越来越多地关注模式识别，包括ECG特征提取和心脏病检测。 SVM最突出的优势可以作为其在简化从高维空间到低尺寸空间的内部产品操作的优异性能，避免在高尺寸空间中的计算。在本研究中，提出了一种利用小波多分辨率分析（WMRA）和SVM的多分类方法。应用WMRA以消除与超出ECG信号频率间隔的频率的干扰（0.05〜100Hz）。同时，WMRA提供不同分解水平的详细系数和近似系数，这是输入到分类的SVM中的输入特征。之后，使用SVM来识别来自MIT-BIH心律失常数据库的6种类型的心脏搏动。此外，讨论并测试了不同的参数C和Y.实验结果表明，随着C的增加，分类性能变得更好。当C和Y设置为1000和0.1时，达到整体分类精度，敏感性和阳性预测性为95.23％，97.42％和97.71％。

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